OpenRaiser/Intern-Atlas huggingface dataset:
FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
Venue: arXiv cs.AI 2025
Year: 2025

Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human -- or superhuman -- expertise? Genuine experts can tackle the hardest problems and push the boundaries of scientific understanding. To illuminate the limits of frontier model capabilities, we turn away from contrived competitive programming puzzles, and instead focus on real-life research problems. We construct FormulaOne, a benchmark that lies at the intersection of graph theory, logic, and algorithms, all well within the training distribution of frontier models. Our problems are incredibly demanding, requiring an array of reasoning steps. The dataset has three key properties. First, it is of commercial interest and relates to practical large-scale optimisation problems, such as those arising in routing, scheduling, and network design. Second, it is generated from the highly expressive framework of Monadic Second-Order (MSO) logic on graphs, paving the way toward automatic problem generation at scale; ideal for building RL environments. Third, many of our problems are intimately related to the frontier of theoretical computer science, and to central conjectures therein, such as the Strong Exponential Time Hypothesis (SETH). As such, any significant algorithmic progress on our dataset, beyond known results, could carry profound theoretical implications. Remarkably, state-of-the-art models like OpenAI's o3 fail entirely on FormulaOne, solving less than 1% of the questions, even when given 10 attempts and explanatory fewshot examples -- highlighting how far they remain from expert-level understanding in some domains. To support further research, we additionally curate FormulaOne-Warmup, offering a set of simpler tasks, from the same distribution. We release the full corpus along with a comprehensive evaluation framework.
VideoITG: Multimodal Video Understanding with Instructed Temporal Grounding
Venue: arXiv cs.CV 2025
Year: 2025

Recent studies have revealed that selecting informative and relevant video frames can significantly improve the performance of Video Large Language Models (Video-LLMs). Current methods, such as reducing inter-frame redundancy, employing separate models for image-text relevance assessment, or utilizing temporal video grounding for event localization, substantially adopt unsupervised learning paradigms, whereas they struggle to address the complex scenarios in long video understanding. We propose Instructed Temporal Grounding for Videos (VideoITG), featuring customized frame sampling aligned with user instructions. The core of VideoITG is the VidThinker pipeline, an automated annotation framework that explicitly mimics the human annotation process. First, it generates detailed clip-level captions conditioned on the instruction; then, it retrieves relevant video segments through instruction-guided reasoning; finally, it performs fine-grained frame selection to pinpoint the most informative visual evidence. Leveraging VidThinker, we construct the VideoITG-40K dataset, containing 40K videos and 500K instructed temporal grounding annotations. We then design a plug-and-play VideoITG model, which takes advantage of visual language alignment and reasoning capabilities of Video-LLMs, for effective frame selection in a discriminative manner. Coupled with Video-LLMs, VideoITG achieves consistent performance improvements across multiple multimodal video understanding benchmarks, showing its superiority and great potentials for video understanding.
Adaptive Linguistic Prompting (ALP) Enhances Phishing Webpage Detection in Multimodal Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Phishing attacks represent a significant cybersecurity threat, necessitating adaptive detection techniques. This study explores few-shot Adaptive Linguistic Prompting (ALP) in detecting phishing webpages through the multimodal capabilities of state-of-the-art large language models (LLMs) such as GPT-4o and Gemini 1.5 Pro. ALP is a structured semantic reasoning method that guides LLMs to analyze textual deception by breaking down linguistic patterns, detecting urgency cues, and identifying manipulative diction commonly found in phishing content. By integrating textual, visual, and URL-based analysis, we propose a unified model capable of identifying sophisticated phishing attempts. Our experiments demonstrate that ALP significantly enhances phishing detection accuracy by guiding LLMs through structured reasoning and contextual analysis. The findings highlight the potential of ALP-integrated multimodal LLMs to advance phishing detection frameworks, achieving an F1-score of 0.93, surpassing traditional approaches. These results establish a foundation for more robust, interpretable, and adaptive linguistic-based phishing detection systems using LLMs.
Low-Light Enhancement via Encoder-Decoder Network with Illumination Guidance
Venue: arXiv cs.CV 2025
Year: 2025

This paper introduces a novel deep learning framework for low-light image enhancement, named the Encoder-Decoder Network with Illumination Guidance (EDNIG). Building upon the U-Net architecture, EDNIG integrates an illumination map, derived from Bright Channel Prior (BCP), as a guidance input. This illumination guidance helps the network focus on underexposed regions, effectively steering the enhancement process. To further improve the model's representational power, a Spatial Pyramid Pooling (SPP) module is incorporated to extract multi-scale contextual features, enabling better handling of diverse lighting conditions. Additionally, the Swish activation function is employed to ensure smoother gradient propagation during training. EDNIG is optimized within a Generative Adversarial Network (GAN) framework using a composite loss function that combines adversarial loss, pixel-wise mean squared error (MSE), and perceptual loss. Experimental results show that EDNIG achieves competitive performance compared to state-of-the-art methods in quantitative metrics and visual quality, while maintaining lower model complexity, demonstrating its suitability for real-world applications. The source code for this work is available at https://github.com/tranleanh/ednig.
Transformer-Based Framework for Motion Capture Denoising and Anomaly Detection in Medical Rehabilitation
Venue: arXiv cs.CV 2025
Year: 2025

This paper proposes an end-to-end deep learning framework integrating optical motion capture with a Transformer-based model to enhance medical rehabilitation. It tackles data noise and missing data caused by occlusion and environmental factors, while detecting abnormal movements in real time to ensure patient safety. Utilizing temporal sequence modeling, our framework denoises and completes motion capture data, improving robustness. Evaluations on stroke and orthopedic rehabilitation datasets show superior performance in data reconstruction and anomaly detection, providing a scalable, cost-effective solution for remote rehabilitation with reduced on-site supervision.
Patterns, Models, and Challenges in Online Social Media: A Survey
Venue: arXiv cs.SI 2025
Year: 2025

The rise of digital platforms has enabled the large scale observation of individual and collective behavior through high resolution interaction data. This development has opened new analytical pathways for investigating how information circulates, how opinions evolve, and how coordination emerges in online environments. Yet despite a growing body of research, the field remains fragmented and marked by methodological heterogeneity, limited model validation, and weak integration across domains. This survey offers a systematic synthesis of empirical findings and formal models. We examine platform-level regularities, assess the methodological architectures that generate them, and evaluate the extent to which current modeling frameworks account for observed dynamics. The goal is to consolidate a shared empirical baseline and clarify the structural constraints that shape inference in this domain, laying the groundwork for more robust, comparable, and actionable analyses of online social systems.
TopicImpact: Improving Customer Feedback Analysis with Opinion Units for Topic Modeling and Star-Rating Prediction
Venue: arXiv cs.CL 2025
Year: 2025

We improve the extraction of insights from customer reviews by restructuring the topic modelling pipeline to operate on opinion units - distinct statements that include relevant text excerpts and associated sentiment scores. Prior work has demonstrated that such units can be reliably extracted using large language models. The result is a heightened performance of the subsequent topic modeling, leading to coherent and interpretable topics while also capturing the sentiment associated with each topic. By correlating the topics and sentiments with business metrics, such as star ratings, we can gain insights on how specific customer concerns impact business outcomes. We present our system's implementation, use cases, and advantages over other topic modeling and classification solutions. We also evaluate its effectiveness in creating coherent topics and assess methods for integrating topic and sentiment modalities for accurate star-rating prediction.
Selective Embedding for Deep Learning
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning has revolutionized many industries by enabling models to automatically learn complex patterns from raw data, reducing dependence on manual feature engineering. However, deep learning algorithms are sensitive to input data, and performance often deteriorates under nonstationary conditions and across dissimilar domains, especially when using time-domain data. Conventional single-channel or parallel multi-source data loading strategies either limit generalization or increase computational costs. This study introduces selective embedding, a novel data loading strategy, which alternates short segments of data from multiple sources within a single input channel. Drawing inspiration from cognitive psychology, selective embedding mimics human-like information processing to reduce model overfitting, enhance generalization, and improve computational efficiency. Validation is conducted using six time-domain datasets, demonstrating that the proposed method consistently achieves high classification accuracy across various deep learning architectures while significantly reducing training times. The approach proves particularly effective for complex systems with multiple data sources, offering a scalable and resource-efficient solution for real-world applications in healthcare, heavy machinery, marine, railway, and agriculture, where robustness and adaptability are critical.
Aligning Knowledge Graphs and Language Models for Factual Accuracy
Venue: arXiv cs.CL 2025
Year: 2025

Large language models like GPT-4, Gemini, and Claude have transformed natural language processing (NLP) tasks such as question answering, dialogue generation, summarization, and so forth; yet their susceptibility to hallucination stands as one of the major challenges. Among numerous approaches to overcome this challenge, integration of Knowledge Graphs (KGs) into language models has emerged as a promising solution as it provides structured, reliable, domain-specific, and up-to-date external information to the language models. In this paper, we introduce ALIGNed-LLM, a simple yet effective approach to improve language models' factuality via a lean strategy to infuse KGs into the latent space of language models inspired by LLaVA where visual and textual information is infused. We use embeddings from a pre-trained Knowledge Graph Embedding (KGE) model, such as TransE, and a trainable projection layer to align entity and text embeddings. This alignment enables the language model to distinguish between similar entities improving factual grounding and reducing hallucination. We tested our approach on three popular questions-answering benchmark datasets alongside language models of varying sizes, showing significant improvement. Furthermore, we applied our approach to a real-world financial use case from a large central bank in Europe, which demands high accuracy and precision, demonstrating a substantial improvement of the LLM answers.
Single- to multi-fidelity history-dependent learning with uncertainty quantification and disentanglement: application to data-driven constitutive modeling
Venue: arXiv cs.LG 2025
Year: 2025

Data-driven learning is generalized to consider history-dependent multi-fidelity data, while quantifying epistemic uncertainty and disentangling it from data noise (aleatoric uncertainty). This generalization is hierarchical and adapts to different learning scenarios: from training the simplest single-fidelity deterministic neural networks up to the proposed multi-fidelity variance estimation Bayesian recurrent neural networks. The versatility and generality of the proposed methodology are demonstrated by applying it to different data-driven constitutive modeling scenarios that include multiple fidelities with and without aleatoric uncertainty (noise). The method accurately predicts the response and quantifies model error while also discovering the noise distribution (when present). This opens opportunities for future real-world applications in diverse scientific and engineering domains; especially, the most challenging cases involving design and analysis under uncertainty.
Lab-Scale Gantry Crane Digital Twin Exemplar
Venue: arXiv cs.GR 2025
Year: 2025

The research topic of digital twins has attracted a large amount of interest over the past decade. However, publicly available exemplars remain scarce. In the interest of open and reproducible science, in this exemplar paper we present a lab-scale gantry crane and its digital twin. The exemplar comprises both the physical and digital side of the twin system. The physical side consists of the physical crane and its controller. The digital side covers the CAD models and kinematic model of the crane, and provides services for optimal control, historical data logging, data visualization and continuous validation. We used this setup as use case in several previous publications where its functionality was validated. It is publicly available and only relies on other freely available and commonly used software, this way we hope it can be used for future research or education on the topic of digital twins.
Paper Summary Attack: Jailbreaking LLMs through LLM Safety Papers
Venue: arXiv cs.CL 2025
Year: 2025

The safety of large language models (LLMs) has garnered significant research attention. In this paper, we argue that previous empirical studies demonstrate LLMs exhibit a propensity to trust information from authoritative sources, such as academic papers, implying new possible vulnerabilities. To verify this possibility, a preliminary analysis is designed to illustrate our two findings. Based on this insight, a novel jailbreaking method, Paper Summary Attack (\llmname{PSA}), is proposed. It systematically synthesizes content from either attack-focused or defense-focused LLM safety paper to construct an adversarial prompt template, while strategically infilling harmful query as adversarial payloads within predefined subsections. Extensive experiments show significant vulnerabilities not only in base LLMs, but also in state-of-the-art reasoning model like Deepseek-R1. PSA achieves a 97\% attack success rate (ASR) on well-aligned models like Claude3.5-Sonnet and an even higher 98\% ASR on Deepseek-R1. More intriguingly, our work has further revealed diametrically opposed vulnerability bias across different base models, and even between different versions of the same model, when exposed to either attack-focused or defense-focused papers. This phenomenon potentially indicates future research clues for both adversarial methodologies and safety alignment.Code is available at https://github.com/233liang/Paper-Summary-Attack
Linking Multi-Site Sex Ad Data at the Individual Level to Aid Counter-Trafficking Efforts
Venue: arXiv cs.SI 2025
Year: 2025

The Internet facilitates sex trafficking through adult service websites (ASWs) that host online advertisements for sexual services (sex ads). Since the closure of the popular site Backpage.com, the ecosystem of ASWs has expanded to include multiple competing sites that are hosted outside US jurisdiction. Gaining intelligence for counter-trafficking efforts requires collecting, linking, and cleaning the data from multiple sites. However, high ad volumes, disparate data types, and the existence of generic and misappropriated data make this process challenging. We present an end-to-end process for linking sex ad data and filtering potentially erroneous links. Outputs of the developed process have been used to inform counter-trafficking operations that have helped identify more than 60 potential victims of sex trafficking, some of whom are getting help to transition out of the life. Our process leverages concepts and techniques from network science, information systems, and artificial intelligence to link ads across sites at the level of an individual or unique posting entity. Our approach is computationally efficient, allowing millions of ads to be processed in under an hour. A key component of our process is an edge filtering procedure that identifies and removes potentially erroneous links in a graph representation of sex ad data. A comparison of the proposed process to an existing approach shows that our process is typically more computationally efficient and yields substantial increases in the number of individuals for which we can derive actionable intelligence. The proposed process is an efficient and effective approach for transforming the high volumes of disparate data from sex ads into intelligence that can save lives. It has been refined over years of collaboration with practitioners and represents a strong foundation upon which further counter-trafficking tools can be built.
Random Variate Generation with Formal Guarantees
Venue: arXiv cs.PL 2025
Year: 2025

This article introduces a new approach to principled and practical random variate generation with formal guarantees. The key idea is to first specify the desired probability distribution in terms of a finite-precision numerical program that defines its cumulative distribution function (CDF), and then generate exact random variates according to this CDF. We present a universal and fully automated method to synthesize exact random variate generators given any numerical CDF implemented in any binary number format, such as floating-point, fixed-point, and posits. The method is guaranteed to operate with the same precision used to specify the CDF, does not overflow, avoids expensive arbitrary-precision arithmetic, and exposes a consistent API. The method rests on a novel space-time optimal implementation for the class of generators that attain the information-theoretically optimal Knuth and Yao entropy rate, consuming the least possible number of input random bits per output variate. We develop a random variate generation library using our method in C and evaluate it on a diverse set of ``continuous'' and ``discrete'' distributions, showing competitive runtime with the state-of-the-art GNU Scientific Library while delivering higher accuracy, entropy efficiency, and automation.
Fake or Real: The Impostor Hunt in Texts for Space Operations
Venue: arXiv cs.LG 2025
Year: 2025

The "Fake or Real" competition hosted on Kaggle (https://www.kaggle.com/competitions/fake-or-real-the-impostor-hunt ) is the second part of a series of follow-up competitions and hackathons related to the "Assurance for Space Domain AI Applications" project funded by the European Space Agency (https://assurance-ai.space-codev.org/ ). The competition idea is based on two real-life AI security threats identified within the project -- data poisoning and overreliance in Large Language Models. The task is to distinguish between the proper output from LLM and the output generated under malicious modification of the LLM. As this problem was not extensively researched, participants are required to develop new techniques to address this issue or adjust already existing ones to this problem's statement.
The Stated Protocol: A Decentralized Framework for Digital Diplomacy
Venue: arXiv cs.CY 2025
Year: 2025

International coordination faces significant friction due to reliance on periodic summits, bilateral consultations, and fragmented communication channels that impede rapid collective responses to emerging global challenges while limiting transparency to constituents. We present the Stated Protocol, a decentralized framework that enables organizations to coordinate through standardized text statements published on their website domains. While applicable to all organizations, this work focuses primarily on the application in international relations, where the protocol enables rapid consensus discovery and collective decision-making without relying on centralized social media platforms. We explore specific applications: (1) faster treaty negotiation through incremental micro-agreements that can be signed digitally within hours rather than months, (2) continuous and transparent operation of international institutions through asynchronous decision-making, (3) coordinated signaling from local governments to national authorities through simultaneous statement publication, and (4) coalition formation among non-governmental organizations through transparent position aggregation.
Checkmate: Zero-Overhead Model Checkpointing via Network Gradient Replication
Venue: arXiv cs.DC 2025
Year: 2025

This paper presents Checkmate, a system that enables per-iteration checkpointing in DNN training without any training slowdown. The traditional approach to checkpointing requires a pause in training to copy model states to a separate location, allowing the state to be restored in the event of failure. This approach fundamentally has a tradeoff between the frequency of checkpoints and the cost of a failure. We avoid this tradeoff; our key insight is that in data-parallel training, all information necessary to create a checkpoint already exists in the network as gradients. Our core contribution is a new multicast abstraction that simultaneously delivers gradients to a separate CPU-based shadow cluster. The shadow maintains a checkpoint by applying those gradients to a copy of the model. Our evaluation shows that Checkmate performs per-iteration checkpointing with training throughput comparable to an ideal no-checkpoint baseline. Checkmate achieves 5 to 34.5x more frequent checkpointing compared to state-of-the-art checkpointing systems, resulting in 80% to 97.1% reduction in repeated work per failure. At the same checkpointing frequency, Checkmate delivers 1.3x to 6.5x throughput compared to other systems.